98 citations · 108 across the 19 of their papers we have counts for
19 papers
FZ-VIS: A Visual Analytics Framework for Quantities-of-Interest-Aware Scientific Lossy Compression
Guoxi Liu, Yuxiao Li, Congrong Ren +6
Modern scientific simulations generate massive volumes of data, making lossy compression essential for efficient storage and transmission. However, preserving critical quantities o…
pMSz: A Distributed Parallel Algorithm for Correcting Extrema and Morse Smale Segmentations in Lossy Compression
Yuxiao Li, Mingze Xia, Xin Liang +7
Lossy compression, widely used by scientists to reduce data from simulations, experiments, and observations, can distort features of interest even under bounded error. Such distort…
FFCz: Fast Fourier Correction for Spectrum-Preserving Lossy Compression of Scientific Data
Congrong Ren, Robert Underwood, Sheng Di +5
This paper introduces a novel technique to preserve spectral features in lossy compression based on a novel fast Fourier correction algorithm\added{ for regular-grid data}. Preserv…
DeepCQ: General-Purpose Deep-Surrogate Framework for Lossy Compression Quality Prediction
Khondoker Mirazul Mumenin, Robert Underwood, Dong Dai +4
Error-bounded lossy compression techniques have become vital for scientific data management and analytics, given the ever-increasing volume of data generated by modern scientific s…
Lossy Compression of Scientific Data: Applications Constrains and Requirements
Franck Cappello, Allison Baker, Ebru Bozda +22
Increasing data volumes from scientific simulations and instruments (supercomputers, accelerators, telescopes) often exceed network, storage, and analysis capabilities. The scienti…
LSHBloom: Memory-efficient, Extreme-scale Document Deduplication
Arham Khan, Robert Underwood, Carlo Siebenschuh +7
Contemporary large language model (LLM) training pipelines require the assembly of internet-scale databases full of text data from a variety of sources (e.g., web, academic, and pu…
LCP: Enhancing Scientific Data Management with Lossy Compression for Particles
Longtao Zhang, Ruoyu Li, Congrong Ren +10
Many scientific applications opt for particles instead of meshes as their basic primitives to model complex systems composed of billions of discrete entities. Such applications spa…
To Compress or Not To Compress: Energy Trade-Offs and Benefits of Lossy Compressed I/O
Grant Wilkins, Sheng Di, Jon C. Calhoun +2
Modern scientific simulations generate massive volumes of data, creating significant challenges for I/O and storage systems. Error-bounded lossy compression (EBLC) offers a solutio…
FRSZ2 for In-Register Block Compression Inside GMRES on GPUs
Thomas Grützmacher, Robert Underwood, Sheng Di +2
The performance of the GMRES iterative solver on GPUs is limited by the GPU main memory bandwidth. Compressed Basis GMRES outperforms GMRES by storing the Krylov basis in low preci…
HoSZp: An Efficient Homomorphic Error-bounded Lossy Compressor for Scientific Data
Tripti Agarwal, Sheng Di, Jiajun Huang +7
Error-bounded lossy compression has been a critical technique to significantly reduce the sheer amounts of simulation datasets for high-performance computing (HPC) scientific appli…
A Survey on Error-Bounded Lossy Compression for Scientific Datasets
Sheng Di, Jinyang Liu, Kai Zhao +23
Error-bounded lossy compression has been effective in significantly reducing the data storage/transfer burden while preserving the reconstructed data fidelity very well. Many error…
Understanding The Effectiveness of Lossy Compression in Machine Learning Training Sets
Robert Underwood, Jon C. Calhoun, Sheng Di +1
Learning and Artificial Intelligence (ML/AI) techniques have become increasingly prevalent in high performance computing (HPC). However, these methods depend on vast volumes of flo…
FedSZ: Leveraging Error-Bounded Lossy Compression for Federated Learning Communications
Grant Wilkins, Sheng Di, Jon C. Calhoun +5
With the promise of federated learning (FL) to allow for geographically-distributed and highly personalized services, the efficient exchange of model updates between clients and se…
Black-Box Statistical Prediction of Lossy Compression Ratios for Scientific Data
Robert Underwood, Julie Bessac, David Krasowska +3
Lossy compressors are increasingly adopted in scientific research, tackling volumes of data from experiments or parallel numerical simulations and facilitating data storage and mov…
ROIBIN-SZ: Fast and Science-Preserving Compression for Serial Crystallography
Robert Underwood, Chun Yoon, Ali Gok +2
Crystallography is the leading technique to study atomic structures of proteins and produces enormous volumes of information that can place strains on the storage and data transfer…
Exploring Lossy Compressibility through Statistical Correlations of Scientific Datasets
David Krasowska, Julie Bessac, Robert Underwood +3
Lossy compression plays a growing role in scientific simulations where the cost of storing their output data can span terabytes. Using error bounded lossy compression reduces the a…
SZ3: A Modular Framework for Composing Prediction-Based Error-Bounded Lossy Compressors
Xin Liang, Kai Zhao, Sheng Di +9
Today's scientific simulations require a significant reduction of data volume because of extremely large amounts of data they produce and the limited I/O bandwidth and storage spac…
cuSZ: An Efficient GPU-Based Error-Bounded Lossy Compression Framework for Scientific Data
Jiannan Tian, Sheng Di, Kai Zhao +8
Error-bounded lossy compression is a state-of-the-art data reduction technique for HPC applications because it not only significantly reduces storage overhead but also can retain h…
FRaZ: A Generic High-Fidelity Fixed-Ratio Lossy Compression Framework for Scientific Floating-point Data
Robert Underwood, Sheng Di, Jon C. Calhoun +1
With ever-increasing volumes of scientific floating-point data being produced by high-performance computing applications, significantly reducing scientific floating-point data size…